In literature, several works proposed Convolutional Neural Network (CNN) based hybrid RNN architectures (CRNN) for HSIC. For instance, [1]} implemented a convolutional RNN in which the first few CONV layers are employed to extract position invariant middle-level features, and then recurrent layers are used to extract spectral-contextual details for HSIC. Similarly, [2]} utilized such a model for semi-supervised HSIC by using pseudo labels. The study [3]} suggested an HSIC framework in which CNN is used to extract spatial features from HSI, then these features are passed to a GRU based fusion network which performs feature level and decision level fusion.
